Lecture 1

# Lecture 1
## Syllabus, Review R
### Psych 10 C
### University of California, Irvine
### 03/28/2022

---

# Welcome to Psych 10 C

---

## Psych 10 C

- This is the third course in the series.

- Instructor: Manuel Villarreal jesusmv1@uci.edu

- TA's: 
 - Adriana Chavez achavezd@uci.edu
 - Arsenii Moskvichev amoskvic@uci.edu
 - Jeff Coon jcoon1@uci.edu
 - Jing Zhang jingz18@uci.edu

---

## Structure of the course

- Goal: learn how and when to use linear models to answer questions about data.

- Evaluation:
  - 5 homework assignments
  - 1 final project
  
--

- Both require the use of R code, however, this is not a coding class so you 
will be able to find what you need on the slides.

- We will provide homework templates that you will need to fill in (except for
the final which will be an empty template).

---

## Grading:

- Each homework will be worth 10 points while the final project is worth 50, for
a total of 100 points.

- Grades will be assigned as follows:

| Grade| Interval     | Grade | Interval     | Grade | Interval   |
|------|--------------|-------|--------------|-------|------------|
| A+   |  100 to 96.5 | A     | 96.4 to 93.5 | A-    | 93.4 to 90 |
| B+   | 89.9 to 86.5 | B     | 86.4 to 83.5 | B-    | 83.4 to 80 |
| C+   | 79.9 to 76.5 | C     | 76.4 to 73.5 | C-    | 73.4 to 70 |
| D+   | 69.9 to 66.5 | D     | 66.4 to 63.5 | D-    | 63.4 to 60 |
| F    | 59.9 to 0    |       |              |       |            |

---

## Discussion sections

- Try to go only to the discussion section that you are registered for, 
otherwise there might not be enough space.

- If you can't make it to your discussion section and would like to go to 
another, consult it with the Ta assigned to your section and the section 
that you plan to attend.

- All discussion sections are in ALP 3600

---

## How to Contact us

- The best way to contact us is via email, we will try to respond within the 
following 24 hrs.

- Try to look at the homework and see if you have any questions. We can't 
promise that we will be able to respond in time if the deadline is close.

- The syllabus is on Canvas, make sure to read it.

---

# Review R

---

## RStudio and RStudio cloud

- There are two ways in which you can get R and RStudio for this class.

1. Install [R](https://cran.microsoft.com/) and [Rstudio](https://www.rstudio.com/products/rstudio/download/#download) on your 
desktop/laptop.
  - First download and install R and then RStudio.

1. Start an account in [RStudio Cloud](https://rstudio.cloud/).

- Both methods are free!

---

## RStudio

- RStudio is a graphical interface for R.

- The window is divided in four parts.

## Set up

- If you have installed R and RStudio in your personal computer then you will 
need a version of LaTex in order to generate the `.pdf` file that you have
to submit for your homework.

- Copy, paste, and then run the following two lines in your RStudio console

```r
# install tinytex package
install.packages('tinytex')

#install tinytex to be able to generate pdf files
tinytex::install_tinytex()
```

- You only need to do this once!

---

## Tidyverse

- For this class we will use many functions that are not part of base R but
that are in a separate package called `tidyverse`.

- To download and install the [tidyverse](https://www.tidyverse.org/packages/)
package you can copy and paste the following line of code into RStudio

```r
# install tidyverse core packages
install.packages("tidyverse")
```

- Again you only need to do this once!

- Packages in R are code that other people have written and made available. This
code takes the form of functions that we can use after downloading the package.

- Many people use `tidyverse` so if you ever have a question and you can't 
reach us, you can Google it!

- I promise someone else has asked that question and there will be a good 
answer.

---

# Loading data into R

---

## Loading data

- When each homework is released we will have a link to the data at the end of 
that days lecture.

- You can use that link to load the data file(s) that you need into your R 
environment.

## Let's start with an example

- We have a data file online which we can load into R using the following lines
of code:

---

## homework data links:

- link for homework problem 1:

```r
# link for data problem 1
link_1 <- "https://github.com/ManuelVU/psych-10c-data/blob/main/hw-problem-1.csv"
```

- link for homework problem 2:

```r
# link for data problem 2
link_2 <- "https://github.com/ManuelVU/psych-10c-data/blob/main/hw-problem-2.csv"
```